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Rungsted. An efficient HMM-based structured prediction model for sequential labeling tasks, with extras.

Project description

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## Rungsted structured perceptron sequential tagger

### Install

The software is installable via PyPI, e.g. do

` pip install rungsted `

### Demo

The repository contains a subset of the part-of-speech tagged Brown corpus. To run the structured perceptron labeler on this dataset, execute:

python src/labeler.py --train data/brown.train --test data/brown.test.vw

Rungsted’s input format is closely modeled on the powerful and flexible format of [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit/wiki/Input-format), with the exception that Rungsted is perfectly fine with labels that are not integers.

### Datasets

Provided you have a working installation of NLTK, you can recreate the Brown dataset with this command.

python rungsted/datasets/cr_brown_pos_data.py data/brown.train.vw data/brown.test.vw

There is also a script rungsted/datasets/conll_to_vw.py to convert from CONLL-formatted input to Rungsted

### Building and uploading to PyPI

First, run python setup.py sdist to generate a source distribution. Then upload the distribution files to PyPI with twine: twine upload dist/*.

To develop locally, use python setup.py develop.

Project details


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